1. Field of the Invention
The present invention relates to a decision management system for creating and applying strategies to manage clients, such as customers, accounts, or applicants, of an organization. More specifically, the present invention relates to a decision management system having automated strategy optimization capabilities.
2. Description of the Related Art
A typical organization maintains a significant amount of information about its clients, where xe2x80x9cclientsxe2x80x9d refers to the customers, accounts or applicants for services or products of the organization. This information can be used effectively, for example, to increase productivity and reduce costs, while achieving the goals of the organization. Such goals may be to improve profitability and maximize customer value.
For example, a company may sell various products to its customers, and may maintain a significant amount of information relating to its customers. This information can be used to improve many critical interactions with the customers, such as marketing communications, sales calls, customer service, collections, and general relationship management activities.
Consider the following examples.
Assume that a diversified financial services company is trying to leverage its customer base by cross-selling its various products. It currently uses limited internal customer information and credit bureau information to identify existing customers for cross-sell campaigns. For example, they might send xe2x80x9cinvitations to applyxe2x80x9d for a home equity loan to those customers who own a mortgage with the company, and meet a minimum credit bureau score threshold. Imagine how much more powerful their cross-selling efforts would be if they could use information from all of the customers"" accounts to offer pre-approved home equity loans to customers where the likelihood of a sale was high, the probability of default was low, and the financial value of that sale was high.
As another example, assume that a regional bell operating company is currently applying only age-based criteria (e.g., xe2x80x9cdays past duexe2x80x9d) to its accounts receivable portfolio to identify candidates for its collections department and to handle those customers. The content of the outbound collection notices and phone calls is driven solely by the age and amount of a customer""s unpaid balance. Imagine if the company had a tool that helped it select and prioritize collection accounts based on the likelihood of a customer interaction making a bottom line difference. Instead of calling or writing all overdue accounts, they could focus resources on those where the customer interaction would make the greatest difference. In addition, they would save the expense and ill will generated by calling customers who would pay without a collections contact.
As a still further example, assume that a manager of a large telephone customer service center for a super-regional bank has been given only hard-line corporate policy to make decisions about fee and rate concessions. While her service reps attempt to stay to the company line, she is deluged with requests from good customers to talk to the manager. She uses her judgment based on the incomplete information available to her to decide which concessions are appropriate to prevent attrition of profitable customers. Just imagine if the service reps had guidelines that were specific to each customer, based upon customer data that indicates their value to the organization, likelihood of attrition, risk level, and other characteristics. The manager could stand by these guidelines with confidence. There would be no concessions made to unprofitable customers, fewer manager overrides, shorter calls, and reduced attrition of the customers they want to keep.
As diverse as the above examples appear on the surface, they share several common characteristics. Each involves a large customer base and a high volume of customer interactions. Each organization has a substantial amount of accumulated data regarding the characteristics, purchasing/behavior patterns, and profitability of customers (though the data may not yet be well organized or analyzed). Each organization has an opportunity to improve performance substantially by treating different customers and customer groups differently, due to diversity in customer relationships and their potential. In each case, there are desired outcomes that could result from alternative customer interactions (e.g., customer purchases a product, pays an outstanding bill, increases deposit balances), and those outcomes can readily be identified, quantified, and tracked.
Therefore, each of the above examples depicts a business situation that currently is not fully benefiting from decision support and therefore is yielding less than optimal results.
There are software based decision management systems in the marketplace which can organize information to make more effective decisions. Generally, a software based decision management system applies strategies to determine actions to be taken, monitors performance based on the taken actions, and refines the strategies in accordance with the monitored performance.
FIG. 1 is a diagram illustrating the general concept of a software-based decision management system. Referring now to FIG. 1, a software based system 10 receives information from operational and/or customer information systems 20, such as, for example, billing systems, account management systems, credit bureau systems and data warehouses. Software based system 10 prioritizes and tailors customer interactions based on predictive information, specific business rules, and continually evolving decision strategies. Software based system 10 then determines an appropriate action which is to be taken by an action-taking system 30. An appropriate action to be taken could include, for example, a call to a customer, a specific collections procedure or a specific marketing action.
A decision management system as in FIG. 1 can provide superior results, such as increased revenue generation, improved cost-effectiveness and enhanced customer relationships.
For example, the American Management Systems (AMS) Strata(trademark) decision support system release 3.0 (hereinafter Strata(trademark) release 3.0) is a software based decision management system which applies predictive modeling techniques to customer data, to thereby generate dramatic improvements in the effectiveness and profitability of customer interactions.
For example, FIG. 2 is a diagram illustrating the functional flow of a decision management system, such as that in Strata(trademark) release 3.0. Referring now to FIG. 2, in step 140, an inbound event is a trigger that is received from one or more external systems to identify that a particular client event has occurred. Here, a client refers to people or entities which interact with, or do business with, an organization. For example, clients include customers, accounts or applicants for services or products of the organization. Each client has associated attributes such as, for example, client age, client balance, etc., which are maintained by the system. An attribute is a data element passed into the decision management system from an external source and/or derived by the decision management system through its own evaluation and processing.
From step 140, the system moves to step 150, where clients are assigned to different segments. A segment is a grouping of clients based on a characteristic by which the clients will be separated for applying different rules. Generally, a segment is a high-level segregation of clients for the purpose of associating largely independent high-level strategy. Thus, segments are separate groups of clients, for which a unique set of evaluation procedures have been defined. For example, a telecommunications company might have a segment for residential customers and another for business customers. Each segment can have, for example, a separate manager who is the only one with security rights to setup or modify the evaluation procedure for that segment.
From step 150, the system moves to step 155, where each segment is further divided into categories. A category is typically a grouping of clients as defined by the organization such that it aligns client interaction/value management objectives. In other words, categories represent groups of clients based on how the organization views the clients. For example, a bank may divide clients (such as credit card holders) into the categories of Bronze, Gold, and Platinum, based on how the bank views the credit worthiness of the clients.
From step 150, the system also moves to step 160, where clients are grouped in a random manner into different test groups for the purpose of applying competing policy rules, strategy, or experiments. Thus, steps 155 and 160 can be seen as being performed in parallel and/or having no inter-dependency.
After steps 155 and 160, each segment has now been divided into test groups and categories. Categories and test groups can be considered to be at the same level in the strategy hierarchy.
From steps 155 and 160, the system moves to step 165, where a matrix is created for each segment, with the categories and test groups on different axes, to create a strategy test cell at the intersection of each category and test group. Here, it is not necessary that a matrix be xe2x80x9cphysicallyxe2x80x9d created. Instead, the data must simply be organized or arranged in some manner that allows clients to be conceptually represented in a data structure equivalent to a matrix, so that clients can be associated with, or assigned to, strategy test cells.
From step 165 the system moves to step 170, where inbound events are matched to function sets.
Function sets are decision logic modules formed by one or more xe2x80x9cfunctions.xe2x80x9d Functions can be, for example, decision trees or score models. There are preferably several different functions which are available in the creation of any function set. One or more functions are typically grouped into function sets when they have comparable objectives (i.e., score cards to predict risk, decision trees to evaluate a credit line, etc.). Therefore, generally, a function set is a reusable business process triggered by one or more events. It may contain one or more strategies (functions) for accomplishing its objective.
From step 170, the system moves to step 180, where the specific function sets for one or more specific inbound events are executed.
From step 180, the system moves to step 190, where the results, or action items, are output.
FIG. 3 is a diagram illustrating an example of a segment being divided into different test groups as in step 160 of FIG. 2. Referring now to FIG. 3, 10% of the segment is randomly assigned to test group 1, 10% of the segment is randomly assigned to test group 2, and 80% of the segment is randomly assigned to test group 3.
FIGS. 4(A) and 4(B) are diagrams illustrating the matching of inbound events to function sets in step 170 of FIG. 2. Referring now to FIG. 4(A), for example, when an inbound event 91 is a credit card campaign, the following function sets are applied, in order: credit card propensity to buy score 92, risk score 93 and offer selection 94. A result 95 of the applied function sets is a determination of whether to send a credit card offer.
Similarly, referring now to FIG. 4(B), for example, when an inbound event 96 is a late payment, the following function sets are applied, in order: risk score 97, underwriting treatment 98 and overdraft decision treatment 99. A result 100 of the applied function sets is a determination whether to send new underwriting and overdraft codes.
FIG. 5 is a diagram illustrating the grouping of functions to function sets. Referring now to FIG. 5, when an inbound event 91 triggers a specific function set, the specific function to be applied to a client will be determined by the test group into which the client was assigned. This allows for strategy experimentation by defining a common sequence of function sets for a given inbound event, but differentiating the actual function that will be invoked for each function set depending on the respective test group into which the client was randomly assigned.
If a function set only contains one function, no experimentation will take place in that function set since every client, regardless of its test group, will be required to use the function. For example, in FIG. 5, no experimentation takes place in the credit card propensity to buy score 92, since this function set contains only one function. By contrast, in FIG. 5, experimentation takes place in offer selection 94, since this function set includes more than one function. This approach provides the strategy analyst with the flexibility to experiment selectively on each strategy component of the overall strategy, as appropriate.
Function sets can include many different types of functions, including, for example, decision trees, score models and matrices. Decision trees are the most common.
FIG. 6 is a diagram illustrating the creation of a matrix of the categories and test groups for a respective segment, as in step 165 of FIG. 2. Referring now to FIG. 6, categories of, for example, Bronze, Gold and Platinum are on one axis of the matrix, and test groups 1, 2 and 3 are on the other axis of the matrix. The intersection of a respective category with a respective test group represents a strategy test cell of the matrix.
Then, possibly for each function set, different strategies are designed for different strategy test cells of the matrix.
FIG. 7 is a diagram illustrating an example of the correspondence of functions of a respective function set to the strategy test cells of the matrix. Referring now to FIG. 7, various function sets, including credit card propensity to buy score 92, risk score 93 and offer selection 94, are executed in a user-defined order upon the occurrence of inbound event 91. Offer selection 94 includes a respective function, which is possibly a decision tree, for each strategy test cell.
As a strategy is designed, the strategy test cells can be examined against each other. Preferably, there is a common set of metrics for the entire matrix, where the metrics are the appropriate measurements against which to measure the performance of the strategy defined for a segment. Then, it can be determined, for example, how well a test group is shifting customers to other categories. For example, it can be determined how quickly test group 1 is moving Bronze customers into the Platinum category in the matrix of FIG. 6. The opposite undesirable effect can also be assessed. Many other types of determinations can be made, based on the various implemented strategies.
The above figures represent the logical flow of how strategy test cells are created, or assigned. However, the systematic or technical flow may be different. Moreover, the logical flow in the above figures represents only one specific example of a decision management system, and decision management systems are not limited to this example. Instead, different decision management systems can have, and likely will have, different logical flows. For example, a decision management system might not assign clients to segments (as in step 150 of FIG. 2), assign clients to categories (as in step 155 of FIG. 2), or create a matrix for each segment (as in step 165 of FIG. 2).
In addition to applying strategies, a decision management system measures performance so that the overall strategy can be appropriately adjusted to optimize results.
For example, FIG. 8 is a diagram illustrating the overall operation of the above-described decision management system for measuring performance. More specifically, FIG. 8 illustrates an example of a data aggregation operation for effectively managing and organizing data.
Referring now to FIG. 8, in step 200, for the above-described decision management system, each path through each decision tree is tagged with a unique identifier referred to as a report group. Although it is preferable to tag each path through each tree so that complex strategy can be created and refined, it is not necessary for each path to be tagged. Instead, the selection of which paths to tag is a matter of design choice, based on the strategy parameters of the decision management system.
Therefore, a report group is a tag which identifies a unique path through a strategy, and is preferably, although not necessarily, applied to terminal nodes of decision trees. A report group is preferably independent of the test group, so that it can be associated with the same branch of comparable trees in two or more test groups. Report groups are a valuable strategy evolution tool, and enable comparative evaluation of strategy effectiveness for categories within a segment. In the present example of a decision management system, categories allow for the analysis of clients who, once being individually evaluated against user-defined criteria, are determined to have similar qualities in consideration of organizational objectives. For example, a category may be defined as all customers who have average current value, high potential value, and a low probability of attrition. Report groups can be placed throughout a decision strategy in order to assure that performance results are accumulated for each respective part of the strategy.
In the present example, all clients in a given report group should be relatively homogenous, the difference being the test group to which the clients were randomly assigned and thus the action/decision applied to the clients being based on their test group. Since report groups are typically independent of test groups, they allow for comparison of the same or alternate categories across experiments (i.e., comparison within the category Platinum of a report group for the test 1 and control test groups). Decision effectiveness reports can then track specified performance metrics (i.e., response rate for marketing, approval rate for underwriting, etc.) by test group for each report group.
Referring again to FIG. 8, from step 200 the system moves to step 210, where observation points are determined. More specifically, in this example, each time a decision is made about a client, that decision is posted. More importantly, in this example, the report group that the client passed through is posted. In addition, in this example, what segment, category, test group, etc. is posted.
From step 210, the system moves to step 220, where performance over time for observation points is accumulated, and matched against the observation points. Generally, an observation point is a snap-shot of a point in time, and has dimensions across which analysis of the data can be performed. A specific client can have multiple observation points. Therefore, in step 210 in FIG. 8, observation points for a client are noted. Then, in step 220, for each client, performance data is matched against observation points. For example, once a month, performance data for a client may be obtained. This performance data is then matched, or correlated, to the appropriate observation points for each account and/or customer.
From step 220, the system moves to step 230, where the collected performance data is periodically aggregated and grouped, preferably, into all possible permutations of the dimensions noted when the observation point was taken and selected for analysis. Generally, in step 230, it is not desirable to report on a specific client, but how well a specific test group or strategy performs. For example, the data is preferably aggregated to determine the performance of segment 1, test group 4, bronze customers, report group B. An aggregate performance data measure can then be determined for all clients meeting this criteria. In this manner, it can be evaluated, for example, how well a certain test group or category performed, instead of how well a specific client performed. Thus, strategy performance can be evaluated, instead of individual client performance.
As a result of the aggregation of data, a row of data having two parts, dimensions and metrics, can be created. Dimensions are the ways the organization wants to view the performance results. For example, segment and category would be dimensions. Aggregating the data in a row allows us to view the intersection of the different points in the matrix created in step 165 of FIG. 2. For example, by aggregating the data, we can view all the metrics, or results, associated with Bronze, test group 2. The users can interactively select which dimensions to apply in filtering the results.
Therefore, the dimensions of the rows should preferably provide all the different ways in which it is intended to analyze the performance data. The dimensions would likely include combinations that allow data relating to the category assignment matrix to be viewed, and combinations that allow data relating to specific strategy paths to be viewed.
For example, a row might typically include the dimensions of segment, test group, category and report group. The metrics for that row should include data relating to those dimensions, such as, for example, delinquency, % credit line used, value, profit. Therefore, by storing dimensions as a xe2x80x9ckeyxe2x80x9d to the data, a xe2x80x9csolution setxe2x80x9d of metrics is obtained which matches that key.
Each row can be thought of as being a unique intersection of values for all dimensional columns. Preferably, the metrics associated with those dimensions are appropriately aggregated for every possible permutation of all of the dimensions. For example, one row can include the dimensions of segment 1, test group 1, category 1, report group 1, and the aggregate results that meet these dimensions. The next row may include the dimensions of segment 1, category 1, test group 1, report group 2, and the aggregate results that meet these dimensions.
When performing the data aggregation operation, all possible permutations of dimensions are preferably determined. Then, the results of clients meeting these dimensions should be matched to these permutations.
For example, FIG. 9 is a diagram illustrating an example of a row of data having a dimensions part and metrics part. Referring now to FIG. 9, each row includes the dimensions of observation time, performance time, segment, test group, category and report group. Preferably, a row is created for each possible permutation of the dimensions. The metrics of delinquency, % credit line used, value and profit are then matched to the various permutations of the dimensions. Generally, the metrics for a specific row should indicate the consolidation all the individual client data of all the individual clients meeting the values of the dimensions identifying that row. Therefore, the data for each specific client is not being reviewed, but instead the performance of a specific strategy is being reviewed.
The use of time dimensions, such as the dimensions of observation time and performance time, allows the movement between categories to be examined over time. Additionally, time allows for trend analysis and selective inclusion of performance points to assess when a strategy performed well/poorly.
Therefore, the data aggregation operation of FIG. 8 prepares and correlates data. In this example, the data aggregation operation can translate the correlated data into a multi-dimensional data model, to support the use of online analytical processing (OLAP) technology. Then, OLAP technology can be applied to evaluate the aggregated data. Generally, OLAP is a known technology that allows for the multi-dimensional analysis of data such that results can be reported in a manner consistent with explaining their significance or inter-relationships. OLAP is based upon the use of multi-dimensional data structures and aggregated data to ensure acceptable performance in leveraging technology. The use of OLAP in a decision management system is described in U.S. application titled USE OF ONLINE ANALYTICAL PROCESSING (OLAP) IN A RULES BASED DECISION MANAGEMENT SYSTEM, U.S. Ser. No. 09/217,016, filed Dec. 21, 1998, and which is incorporated herein by reference.
FIG. 10 is a diagram illustrating an example of a definition hierarchy of a decision management system, and provides a version level for creating different strategy versions. Referring now to FIG. 10, a version level can be interjected between a system level and a segment level. A function level is shown as being under the version level and at the same level as segment. Thus, in FIG. 10, different functions are associated with different versions and functions are associated with specific segments. Levels and associations provide the user with the ability to organize the strategy components of a strategy.
While FIG. 10 illustrates a versioning level interjected between the system level and the segment level, a versioning level can be virtually at any level in the definition hierarchy. For example, FIG. 11(A) is a diagram illustrating a definition hierarchy having the version level beneath the system level and the function level. In addition, version levels can exist simultaneously at multiple levels in the definition hierarchy. For example, FIG. 11(B) is a diagram illustrating a definition hierarchy having a version level above and beneath the function level. The use of versioning levels in a decision management system is described, for example, in U.S. application titled VERSIONING IN A RULES BASED DECISION MANAGEMENT SYSTEM, U.S. Ser. No. 09/219,341, filed Dec. 23, 1998, and which is incorporated herein by reference.
The above-described decision management system can allow hybrid strategies to be developed, based on the success of different experiments.
For example, FIG. 12 is a diagram illustrating the effectiveness of creating a hybrid strategy in a decision management system. Referring now to FIG. 12, a xe2x80x9ctestxe2x80x9d strategy is applied to test group A, where customers in test group A are divided into two groups, TGA1 and TGA2. Group TGA1 includes all customers less than 40 years old. Group TGA2 includes all customers greater than or equal to 40 years old. A letter is sent to customers whether they are in group TGA1 or TGA2. The end result is that a letter is 60% effective for the customers in TGA1, and 70% effective for customers in TGA2. Assuming that 50% of the population is greater than or equal to 40 years old, and 50% of the population is less than 40 years old, the overall success rate of the test strategy is 65%.
Similarly, a xe2x80x9ccontrolxe2x80x9d strategy is applied to test group B, where customers in test group B are divided into two groups, TGB1 and TGB2. Group TGB1 includes all customers less than 40 years old. Group TGB2 includes all customers greater than or equal to 40 years old. A call is made to customers whether they are in group TGB1 or TGB2. The end result is that a call is 50% effective for the customers in TGB1, and 90% effective for customers in TGB2. Assuming that 50% of the population is greater than or equal to 40 years old, and 50% of the population is less than 40 years old, the overall success rate of the control strategy is 70%.
An overall comparison of results of test group A (the xe2x80x9ctestxe2x80x9d strategy) versus test group B (the xe2x80x9ccontrolxe2x80x9d group) indicates that the control strategy is superior, as measured by overall success rate. However, when strategy effectiveness is reported at the comparable path level through the test and control strategies, it is possible to build a new hybrid strategy that will outperform either the test strategy or the control strategy by combining the best performing actions of each strategy. For example, the hybrid strategy would send a letter to all customers less than 40 years old, but call all customers greater than or equal to 40 years old. Such a hybrid strategy should produce an expected overall success rate of 75%, which is higher than either of the test or control strategies.
Such an approach for determining a hybrid strategy could be used, for example, to improve the strategy in offer selection 94 in FIG. 5, where different strategies are applied to different test groups. The formation of a hybrid strategy can significantly increase the effectiveness and profitability of an organization.
As can be seen from above, software based decision management systems apply strategies to determine actions to be taken, monitor performance based on the taken actions, and refine the strategies in accordance with the monitored performance.
Moreover, as can be seen from above, a strategy is formed of many different strategy components. Here, a strategy component refers to any part of a strategy implemented in a decision management system. For example, a strategy component can be a system, version, attribute, inbound event, outbound event, function, function set, segment, report instruction, continuous dimension, test group or report group.
FIG. 13 is a diagram illustrating the analysis of performance results and the recommendation of strategy changes in a conventional decision management system. Referring now to FIG. 13, in step 500, a rules editor creates and edits rules which define the strategy. From step 500, the operation moves to step 502 where a decision engine applies the strategies created and edited by the rules editor. From step 502, the process moves to step 504, where performance reporting is done, to report strategy/policy performance results to an end user of the system, such as a strategy analyst. The performance reporting may include the use of OLAP technology, as previously described.
Then, from step 504, the operation moves to step 506 where the performance results are analyzed, and strategy changes are recommend in accordance with the analyzed performance results. Step 506 is performed by the end user. Thus, the end user must manually analyze the performance results, and somehow determine what strategy changes should be recommended. This is a very complex, difficult and time consuming process. For example, it could take a very long time for the end user to analyze manually the performance results. Moreover, it may be very difficult for the end user to recognize trends or implications of the performance results. In addition, it may be very difficult for an end user to think of appropriate changes, especially if there is a large amount of performance data and potential options.
Step 506 in FIG. 13 is shown as a xe2x80x9c?,xe2x80x9d since this step includes a significant human factor in analyzing the complicated performance results and recommending strategy changes. For example, in step 506, different end users will typically analyze the performance results in a different manner and recommend different strategy changes, and thereby potentially introduce inconsistencies.
Therefore, a conventional decision management system is limited in its ability to analyze performance results and recommend strategy changes in an efficient, effective, and consistent manner, particularly without ongoing human intervention.
Therefore, it is an object of the present invention to provide a decision management system which can analyze performance results and recommend strategy changes in an efficient, effective, and consistent manner.
Objects of the present invention are achieved by providing a computer-implemented decision management process including (a) applying a decision management strategy; (b) determining results of the applied strategy; and (c) automatically optimizing at least a part of the strategy in accordance with the determined results.
In addition, objects of the present invention are achieved by providing a computer-implemented decision management process including (a) applying a decision management strategy formed of a plurality of strategy components; (b) determining results of the applied strategy; (c) selecting a strategy component by an end user of the process; (d) selecting criteria by the end user for optimizing the selected strategy component; and (e) automatically optimizing the strategy in accordance with the determined results, the selected strategy component and the selected criteria.
Additional objects and advantages of the invention will be set forth in part in the description which follows, and, in part, will be obvious from the description, or may be learned by practice of the invention.